Efficient Sampling Algorithms for Approximate Temporal Motif Counting (Extended Version)
Jingjing Wang, Yanhao Wang, Wenjun Jiang, Yuchen Li and, Kian-Lee Tan

TL;DR
This paper introduces efficient approximate algorithms for counting temporal motifs in large temporal graphs, leveraging random sampling techniques to improve performance and scalability over existing methods.
Contribution
It proposes a generic edge sampling algorithm and an improved hybrid EWS algorithm for counting temporal motifs, with theoretical analysis and extensive experimental validation.
Findings
Higher efficiency compared to state-of-the-art methods
Better accuracy in motif counting
Greater scalability on real-world datasets
Abstract
A great variety of complex systems ranging from user interactions in communication networks to transactions in financial markets can be modeled as temporal graphs, which consist of a set of vertices and a series of timestamped and directed edges. Temporal motifs in temporal graphs are generalized from subgraph patterns in static graphs which take into account edge orderings and durations in addition to structures. Counting the number of occurrences of temporal motifs is a fundamental problem for temporal network analysis. However, existing methods either cannot support temporal motifs or suffer from performance issues. In this paper, we focus on approximate temporal motif counting via random sampling. We first propose a generic edge sampling (ES) algorithm for estimating the number of instances of any temporal motif. Furthermore, we devise an improved EWS algorithm that hybridizes edge…
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Taxonomy
TopicsComplex Network Analysis Techniques · Peer-to-Peer Network Technologies · Data Management and Algorithms
